Summary

Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Green, and Carroll have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not yet advanced statistics majors. Their text accomplishes this through a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through geometric presentation. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with matrix algebra.

Benefits:

Uses geometric interpretation to develop readers' intuition and provide students with a mental picture of how each method works. Mathematics is used to support the underlying intuition.

Takes a pragmatic, hands-on approach through sample problems based on real data. Each chapter offers at least one application, as well as a discussion of issues related to the proper interpretation of the results.

Accompanying student workbooks are specific to a given software package (either SAS or SPSS), and annotated program output facilitates interpretation and provides links to concepts included in the text.

Addresses important issues that come up with each application of a method. Special emphasis is placed on generalizing the results of the analysis, and suggestions are presented for testing the validity of findings.

Contains illustrations and sample problems from a wide range of areas, including psychology, sociology, and marketing research.

Follows a standard format in each chapter. This format begins by discussing a general set of research objectives, followed by some illustrative examples of problems in different areas. Then it provides an explanation of how each methods works, followed by a sample problem, application of the technique, and interpretation of results.

Author Bio

Lattin, Jim : Stanford University

Carroll, Doug : Rutgers University

Green, Paul : University of Pennsylvania

Table of Contents

1. INTRODUCTION

The Nature of Multivariate Data Overview of Multivariate Methods Format of Succeeding Chapters

Introduction Metric MDS: How Does it Work? Non-Metric MDS: How Does it Work? Individual Differences Scaling: How Does It Work? Centroid Scaling: How Does it Work? A Note on Model Validation Learning Summary Exercises

8. CLUSTERING

Introduction Objectives of Cluster Analysis Measures of Distance, Dissimilarity, and Density Agglomerative Clustering: How IT Works Partitioning: How it Works Sample Problem: Preference Segmentation Questions Regarding the Application of Cluster Analysis Learning Summary Exercises

Introduction Structural Equations with Latent Variables: How Does it Work? Sample Problem: Modeling the Adoption of Innovation Questions Regarding the Application of Structural Equations with Latent Variables Learning Summary Exercises

11. ANALYSIS OF VARIANCE

Introduction ANOLVA and ANCOVA: How Does it Work? Sample Problem: Test Marketing a New Product Multiple Analysis of Variance (MANOVA): How Does it Work Sample Problem: Testing Advertising Message Strategy Questions Regarding the Application of MANOVA and MANCOVA Learning Summary Exercises

Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Green, and Carroll have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not yet advanced statistics majors. Their text accomplishes this through a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through geometric presentation. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with matrix algebra.

Benefits:

Uses geometric interpretation to develop readers' intuition and provide students with a mental picture of how each method works. Mathematics is used to support the underlying intuition.

Takes a pragmatic, hands-on approach through sample problems based on real data. Each chapter offers at least one application, as well as a discussion of issues related to the proper interpretation of the results.

Accompanying student workbooks are specific to a given software package (either SAS or SPSS), and annotated program output facilitates interpretation and provides links to concepts included in the text.

Addresses important issues that come up with each application of a method. Special emphasis is placed on generalizing the results of the analysis, and suggestions are presented for testing the validity of findings.

Contains illustrations and sample problems from a wide range of areas, including psychology, sociology, and marketing research.

Follows a standard format in each chapter. This format begins by discussing a general set of research objectives, followed by some illustrative examples of problems in different areas. Then it provides an explanation of how each methods works, followed by a sample problem, application of the technique, and interpretation of results.

Introduction Metric MDS: How Does it Work? Non-Metric MDS: How Does it Work? Individual Differences Scaling: How Does It Work? Centroid Scaling: How Does it Work? A Note on Model Validation Learning Summary Exercises

8. CLUSTERING

Introduction Objectives of Cluster Analysis Measures of Distance, Dissimilarity, and Density Agglomerative Clustering: How IT Works Partitioning: How it Works Sample Problem: Preference Segmentation Questions Regarding the Application of Cluster Analysis Learning Summary Exercises

Introduction Structural Equations with Latent Variables: How Does it Work? Sample Problem: Modeling the Adoption of Innovation Questions Regarding the Application of Structural Equations with Latent Variables Learning Summary Exercises

11. ANALYSIS OF VARIANCE

Introduction ANOLVA and ANCOVA: How Does it Work? Sample Problem: Test Marketing a New Product Multiple Analysis of Variance (MANOVA): How Does it Work Sample Problem: Testing Advertising Message Strategy Questions Regarding the Application of MANOVA and MANCOVA Learning Summary Exercises

Summary

Offering the latest teaching and practice of applied multivariate statistics, this text is perfect for students who need an applied introduction to the subject. Lattin, Green, and Carroll have created a text that speaks to the needs of applied students who have advanced beyond the beginning level, but are not yet advanced statistics majors. Their text accomplishes this through a three-part structure. First, the authors begin each major topic by developing students' statistical intuition through geometric presentation. Then, they providing illustrative examples for support. Finally, for those courses where it will be valuable, they describe relevant mathematical underpinnings with matrix algebra.

Benefits:

Uses geometric interpretation to develop readers' intuition and provide students with a mental picture of how each method works. Mathematics is used to support the underlying intuition.

Takes a pragmatic, hands-on approach through sample problems based on real data. Each chapter offers at least one application, as well as a discussion of issues related to the proper interpretation of the results.

Accompanying student workbooks are specific to a given software package (either SAS or SPSS), and annotated program output facilitates interpretation and provides links to concepts included in the text.

Addresses important issues that come up with each application of a method. Special emphasis is placed on generalizing the results of the analysis, and suggestions are presented for testing the validity of findings.

Contains illustrations and sample problems from a wide range of areas, including psychology, sociology, and marketing research.

Follows a standard format in each chapter. This format begins by discussing a general set of research objectives, followed by some illustrative examples of problems in different areas. Then it provides an explanation of how each methods works, followed by a sample problem, application of the technique, and interpretation of results.

Author Bio

Lattin, Jim : Stanford University

Carroll, Doug : Rutgers University

Green, Paul : University of Pennsylvania

Table of Contents

Table of Contents

1. INTRODUCTION

The Nature of Multivariate Data Overview of Multivariate Methods Format of Succeeding Chapters

Introduction Metric MDS: How Does it Work? Non-Metric MDS: How Does it Work? Individual Differences Scaling: How Does It Work? Centroid Scaling: How Does it Work? A Note on Model Validation Learning Summary Exercises

8. CLUSTERING

Introduction Objectives of Cluster Analysis Measures of Distance, Dissimilarity, and Density Agglomerative Clustering: How IT Works Partitioning: How it Works Sample Problem: Preference Segmentation Questions Regarding the Application of Cluster Analysis Learning Summary Exercises

Introduction Structural Equations with Latent Variables: How Does it Work? Sample Problem: Modeling the Adoption of Innovation Questions Regarding the Application of Structural Equations with Latent Variables Learning Summary Exercises

11. ANALYSIS OF VARIANCE

Introduction ANOLVA and ANCOVA: How Does it Work? Sample Problem: Test Marketing a New Product Multiple Analysis of Variance (MANOVA): How Does it Work Sample Problem: Testing Advertising Message Strategy Questions Regarding the Application of MANOVA and MANCOVA Learning Summary Exercises